This paper addresses the classification of Arabic text data in the field of
Natural Language Processing (NLP), with a particular focus on Natural Language
Inference (NLI) and Contradiction Detection (CD). Arabic is considered a
resource-poor language, meaning that there are few data sets available, which
leads to limited availability of NLP methods. To overcome this limitation, we
create a dedicated data set from publicly available resources. Subsequently,
transformer-based machine learning models are being trained and evaluated. We
find that a language-specific model (AraBERT) performs competitively with
state-of-the-art multilingual approaches, when we apply linguistically informed
pre-training methods such as Named Entity Recognition (NER). To our knowledge,
this is the first large-scale evaluation for this task in Arabic, as well as
the first application of multi-task pre-training in this context.

本文主要研究阿拉伯语文本数据的分类，特别关注自然语言推理和矛盾检测。通过创建专用数据集并利用语言学上知识进行预训练，发现了具有竞争力的阿拉伯语特定模型（AraBERT），并成功应用于大规模任务评估及多任务预训练的首个阿拉伯语背景应用。